Efficient parallel algorithms
Reasoning about knowledge
Model Checking Knowledge and Time in Systems with Perfect Recall (Extended Abstract)
Proceedings of the 19th Conference on Foundations of Software Technology and Theoretical Computer Science
On the undecidability of probabilistic planning and related stochastic optimization problems
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Introduction to probabilistic automata (Computer science and applied mathematics)
Introduction to probabilistic automata (Computer science and applied mathematics)
Automatic verification of probabilistic concurrent finite state programs
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
Model Checking Knowledge and Linear Time: PSPACE Cases
LFCS '07 Proceedings of the international symposium on Logical Foundations of Computer Science
Diagnosers and diagnosability of succinct transition systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Probabilistic automata on finite words: decidable and undecidable problems
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Symbolic model checking of probabilistic knowledge
Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Knowledge
Bounded planning for strategic goals with incomplete information and perfect recall
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
A logic of probabilistic knowledge and strategy
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Diagnosability is a key attribute of systems to enable the detection of failure events by partial observations. This paper addresses the diagnosability in concurrent probabilistic systems. Four different notions (L-, P-, A-, and AA-diagnosability) are characterised by formulas of a logic of knowledge, time and probability. Also, we investigate the computational complexities of verifying them: the L-diagnosability is NL-complete, the A-diagnosability is PTIME-complete, and the P-diagnosability is in PSPACE.